In recent years, a face detection technique of detecting a position and a direction of the face and a state of face parts such as the eyes and the mouth included in a captured still image or a moving image has been developed. For example, in a vehicle, inattentive driving or dozing-off while driving is detected by detecting the driver's face, and thus a predetermined action such as triggering an alarm can be performed.
Stan Z. Li, Anil K. Jain, “Handbook of Face Recognition”, Springer, 2011, p. 124 to 133 (Reference 1) discloses a face detection technique (active shape model: ASM, or active appearance model: AAM) of generating a model of a face in an image by fitting a statistical face model to the face in the image, that is, performing model fitting by using a steepest descent method or the like. A direction of the face or a state of a face part can be estimated by modeling the face in the image by using this technique, and time variation in the face and the face part can be monitored by updating (tracking) the model over time.
In the technique disclosed in Reference 1, accuracy of the model fitting is greatly influenced by an initial state of a model, that is, where the model is initially disposed in an image and which angle and shape the model is set to. If the initial state of the model is widely different from an actual state of the face, there is a case where calculation of model fitting for fitting the model to the actual face ends with a local optimum solution, and thus the model deviates from the actual face and converges. This is referred to as a fitting error, and if the fitting error occurs, accuracy of a model of the face is reduced. Particularly, positions of the eyes of the model tend to wrongly converge on positions of glass frames or the eyebrows.